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Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...

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使用相位成像元传感器进行细胞分类.

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    本研究介绍了一种混合光电子网络,使用角度敏感的超表面光探测器来取代卷积神经网络 (CNN) 的第一层. 这种方法显著降低了像癌细胞识别这样的图像分类任务的计算成本.

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    科学领域:

    • 光电学是指光电子产品.
    • 机器学习 机器学习
    • 计算成像技术的成像

    背景情况:

    • 光子技术为减少机器学习中的计算成本提供了一条途径.
    • 卷积神经网络 (CNN) 被广泛用于图像分类.
    • 超表面光探测器正在成为新的光学元件.

    研究的目的:

    • 为了研究混合光电子CNN架构用于图像分类.
    • 为了评估在CNN中对角度敏感的超表面光探测器的性能.
    • 评估降低图像分类中的计算负载的潜力.

    主要方法:

    • 将CNN的第一个卷积层替换为一组图像传感器阵列的角度敏感的超表面光探测器.
    • 通过记录异型边缘增强图像来可视化透明相位物体.
    • 评估使用计算机成像模拟用于癌细胞识别的分类性能.

    主要成果:

    • 混合光电子网络准确地对透明的癌细胞进行分类 (准确率高于90%).
    • 该系统直接可视化相位物体,模仿CNN的特征地图.
    • 与完全数字的CNN相比,计算的数量减少了数量级.

    结论:

    • 混合光电子网络集成地表光探测器显示出对高效图像分类的承诺.
    • 这种方法可显著降低机器学习任务的计算成本.
    • 开发的系统在识别透明的生物样本方面表现出高度准确性.